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Topological Data Analysis for the Working Data Scientist

The talk below, “Topological Data Analysis for the Working Data Scientist” was presented at the SF Data Mining meetup group. Speaker Anthony Bak begins with a short review of the Mapper algorithm and discuss how to think about problems in the topological framework. Through a series of examples he shows how TDA extends and improves many existing data analysis techniques in both supervised and unsupervised settings, discusses how it can be used to correct machine learning models, and how it offers the ability to create unique topological models.

 

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